GENERALIZED LINEAR MIXED MODELS - AN OVERVIEW

Generalized linear models provide a methodology for doing regression and ANOV A-type analysis with data whose errors are not necessarily normally-distributed. Common applications in agriculture include categorical data, survival analysis, bioassay, etc. Most of the literature and most of the available computing software for generalized linear models applies to cases in which all model effects are fixed. However, many agricultural research applications lead to mixed or random effects models: split-plot experiments, animal- and plant-breeding studies, multi-location studies, etc. Recently, through a variety of efforts in a number of contexts, a general framework for generalized linear models with random effects, the generalized linear mixed model, has been developed . The purpose of this presentation is to present an overview of the methodology for generalized mixed linear models. Relevant background, estimating equations, and general approaches to interval estimation and hypothesis testing will be presented. Methods will be illustrated via a small data set involving binary data

THRESHOLD SIRE MODELS FOR ESTIMATING GENETIC PARAMETERS FOR STAYABILITY IN BEEF COWS

Stayability is the ability of a beef cow to remain in production to a specified age. In this study, the interest was in determining the genetic relationship between stayability to an early age with stayability to a later age. A nested threshold sire model for stayability was used here to estimate the genetic relationship between stayability to different ages. Genetic correlations were estimated among six different stayability traits using records from 1,868 Hereford cows. The model included period and year of birth as fixed factors and sire as a random factor. The numerator relationship matrix accounted for all known relationships among sires. Penalized quasilikelihood estimates were obtained using a probit link function. Estimates of heritability on the original scale were small and ranged from 0.09 to 0.17. Estimates of genetic correlations were low to moderate and variable in sign. Results indicate that selection for stayability to an early age would have a limited impact on stayability to later ages

GENOMICS SYMPOSIUM: Using genomic approaches to uncover sources of variation in age at puberty and reproductive longevity in sows

Genetic variants associated with traits such as age at puberty and litter size could provide insight into the underlying genetic sources of variation impacting sow reproductive longevity and productivity. Genomewide characterization and gene expression profiling were used using gilts from the University of Nebraska–Lincoln swine resource population (n = 1,644) to identify genetic variants associated with age at puberty and litter size traits. From all reproductive traits studied, the largest fraction of phenotypic variation explained by the Porcine SNP60 BeadArray was for age at puberty (27.3%). In an evaluation data set, the predictive ability of all SNP from highranked 1-Mb windows (1 to 50%), based on genetic variance explained in training, was greater (12.3 to 36.8%) compared with the most informative SNP from these windows (6.5 to 23.7%). In the integrated data set (n = 1,644), the top 1% of the 1-Mb windows explained 6.7% of the genetic variation of age at puberty. One of the high-ranked windows detected (SSC2, 12–12.9 Mb) showed pleiotropic features, affecting both age at puberty and litter size traits. The RNA sequencing of the hypothalami arcuate nucleus uncovered 17 differentially expressed genes (adjusted P \u3c 0.05) between gilts that became pubertal early (180 d of age). Twelve of the differentially expressed genes are upregulated in the late pubertal gilts. One of these genes is involved in energy homeostasis (FFAR2), a function in which the arcuate nucleus plays an important contribution, linking nutrition with reproductive development. Energy restriction during the gilt development period delayed age at puberty by 7 d but increased the probability of a sow to produce up to 3 parities (P \u3c 0.05). Identification of pleotropic functional polymorphisms may improve accuracy of genomic prediction while facilitating a reduction in sow replacement rates and addressing welfare concerns

Synaptogyrin-2 influences replication of Porcine circovirus 2

Porcine circovirus 2 (PCV2) is a circular single-stranded DNA virus responsible for a group of diseases collectively known as PCV2 Associated Diseases (PCVAD). Variation in the incidence and severity of PCVAD exists between pigs suggesting a host genetic component involved in pathogenesis. A large-scale genome-wide association study of experimentally infected pigs (n = 974), provided evidence of a host genetic role in PCV2 viremia, immune response and growth during challenge. Host genotype explained 64% of the phenotypic variation for overall viral load, with two major Quantitative Trait Loci (QTL) identified on chromosome 7 (SSC7) near the swine leukocyte antigen complex class II locus and on the proximal end of chromosome 12 (SSC12). The SNP having the strongest association, ALGA0110477 (SSC12), explained 9.3% of the genetic and 6.2% of the phenotypic variance for viral load. Dissection of the SSC12 QTL based on gene annotation, genomic and RNA-sequencing, suggested that a missense mutation in the SYNGR2 (SYNGR2 p.Arg63Cys) gene is potentially responsible for the variation in viremia. This polymorphism, located within a protein domain conserved across mammals, results in an amino acid variant SYNGR2 p.63Cys only observed in swine. PCV2 titer in PK15 cells decreased when the expression of SYNGR2 was silenced by specific-siRNA, indicating a role of SYNGR2 in viral replication. Additionally, a PK15 edited clone generated by CRISPR-Cas9, carrying a partial deletion of the second exon that harbors a key domain and the SYNGR2 p.Arg63Cys, was associated with a lower viral titer compared to wildtype PK15 cells (\u3e24 hpi) and supernatant (\u3e48hpi)(P \u3c 0.05). Identification of a non-conservative substitution in this key domain of SYNGR2 suggests that the SYNGR2 p.Arg63Cys variant may underlie the observed genetic effect on viral load

The roles of age at puberty and energy restriction |in sow reproductive longevity: a genomic perspective

Approximately 50% of sows are culled annually with more than one-third due to poor fertility. Our research demonstrated that age at puberty is an early pre-breeding indicator of reproductive longevity. Age at puberty can be measured early in life, has a moderate heritability, and is negatively correlated with lifetime number of parities. Detection of age at puberty is tedious and time consuming and is therefore not collected by the industry, which limits genetic progress. Genomic prediction is a viable approach to preselect gilts that will express puberty early and have superior reproductive longevity. The hypothesis that genetic variants explaining differences in age at puberty also explain differences in sow reproductive longevity was tested. Phenotypes, genotypes, and tissues from the UNL resource population (n \u3e 1700) were used in genome-wide association analyses, genome, and RNA sequencing to uncover functional polymorphisms that could explain variation in puberty and reproductive longevity. A BeadArray including 56,424 SNP explained 25.2% of the phenotypic variation in age at puberty in a training set (n = 820). Evaluation of major windows and SNPs of subsequent batches of similar genetics (n = 412) showed that if all SNPs located in the major 1-Mb windows were tested, they explained a substantial amount of phenotypic variation (12.3 to 36.8%). Due to differences in linkage disequilibrium status, the most informative SNP from these windows explained a lower proportion of the variation (6.5 to 23.7%). To improve genomic predictive ability, the limited capability of BeadArray was enhanced by potential functional variants uncovered by genome sequencing of selected sires (n = 20; \u3e20X). There were 11.2 mil. SNPs and 2.9 mil. indels discovered across sires and reference genomes. The role of gene expression differences in explaining phenotypic variation in age at puberty was investigated by RNA sequencing of the hypothalamic arcuate nucleus (ARC) in gilts (n = 37) with different pubertal statuses. Seventy genes, including genes involved in reproductive processes, were differentially expressed between gilts with early and late puberty status (Padj \u3c 0.1). Dietary restriction of energy 3 mo before breeding delayed puberty by 7 d but improved the potential of a sow producing up to three parities (P \u3c 0.05). Energy restriction was associated with differential expression in 42 genes in the ARC, including genes involved in energy metabolism. This integrated genomic information will be evaluated in commercial populations to improve the reproductive potential of sows through genomic selection. This project is supported by AFRI Competitive grant no. 2013-68004-20370 from the USDA-NIFA. USDA is an equal opportunity provider and employer

Genome-wide association study of disease resilience traits from a natural polymicrobial disease challenge model in pigs identifies the importance of the major histocompatibility complex region

Infectious diseases cause tremendous financial losses in the pork industry, emphasizing the importance of disease resilience, which is the ability of an animal to maintain performance under disease. Previously, a natural polymicrobial disease challenge model was established, in which pigs were challenged in the late nursery phase by multiple pathogens to maximize expression of genetic differences in disease resilience. Genetic analysis found that performance traits in this model, including growth rate, feed and water intake, and carcass traits, as well as clinical disease phenotypes, were heritable and could be selected for to increase disease resilience of pigs. The objectives of the current study were to identify genomic regions that are associated with disease resilience in this model, using genome-wide association studies and fine-mapping methods, and to use gene set enrichment analyses to determine whether genomic regions associated with disease resilience are enriched for previously published quantitative trait loci, functional pathways, and differentially expressed genes subject to physiological states. Multiple quantitative trait loci were detected for all recorded performance and clinical disease traits. The major histocompatibility complex region was found to explain substantial genetic variance for multiple traits, including for growth rate in the late nursery (12.8%) and finisher (2.7%), for several clinical disease traits (up to 2.7%), and for several feeding and drinking traits (up to 4%). Further fine mapping identified 4 quantitative trait loci in the major histocompatibility complex region for growth rate in the late nursery that spanned the subregions for class I, II, and III, with 1 single-nucleotide polymorphism in the major histocompatibility complex class I subregion capturing the largest effects, explaining 0.8–27.1% of genetic variance for growth rate and for multiple clinical disease traits. This singlenucleotide polymorphism was located in the enhancer of TRIM39 gene, which is involved in innate immune response. The major histocompatibility complex region was pleiotropic for growth rate in the late nursery and finisher, and for treatment and mortality rates. Growth rate in the late nursery showed strong negative genetic correlations in the major histocompatibility complex region with treatment or mortality rates (–0.62 to –0.85) and a strong positive genetic correlation with growth rate in the finisher (0.79). Gene set enrichment analyses found genomic regions associated with resilience phenotypes to be enriched for previously identified disease susceptibility and immune capacity quantitative trait loci, for genes that were differentially expressed following bacterial or virus infection and immune response, and for gene ontology terms related to immune and inflammatory response. In conclusion, the major histocompatibility complex and other quantitative trait loci that harbor immune-related genes were identified to be associated with disease resilience traits in a large-scale natural polymicrobial disease challenge. The major histocompatibility complex region was pleiotropic for growth rate under challenge and for clinical disease traits. Four quantitative trait loci were identified across the class I, II, and III subregions of the major histocompatibility complex for nursery growth rate under challenge, with 1 single-nucleotide polymorphism in the major histocompatibility complex class I subregion capturing the largest effects. The major histocompatibility complex and other quantitative trait loci identified play an important role in host response to infectious diseases and can be incorporated in selection to improve disease resilience, in particular the identified singlenucleotide polymorphism in the major histocompatibility complex class I subregion

Synaptogyrin-2 influences replication of Porcine circovirus 2

Porcine circovirus 2 (PCV2) is a circular single-stranded DNA virus responsible for a group of diseases collectively known as PCV2 Associated Diseases (PCVAD). Variation in the incidence and severity of PCVAD exists between pigs suggesting a host genetic component involved in pathogenesis. A large-scale genome-wide association study of experimentally infected pigs (n = 974), provided evidence of a host genetic role in PCV2 viremia, immune response and growth during challenge. Host genotype explained 64% of the phenotypic variation for overall viral load, with two major Quantitative Trait Loci (QTL) identified on chromosome 7 (SSC7) near the swine leukocyte antigen complex class II locus and on the proximal end of chromosome 12 (SSC12). The SNP having the strongest association, ALGA0110477 (SSC12), explained 9.3% of the genetic and 6.2% of the phenotypic variance for viral load. Dissection of the SSC12 QTL based on gene annotation, genomic and RNA-sequencing, suggested that a missense mutation in the SYNGR2 (SYNGR2 p.Arg63Cys) gene is potentially responsible for the variation in viremia. This polymorphism, located within a protein domain conserved across mammals, results in an amino acid variant SYNGR2 p.63Cys only observed in swine. PCV2 titer in PK15 cells decreased when the expression of SYNGR2 was silenced by specific-siRNA, indicating a role of SYNGR2 in viral replication. Additionally, a PK15 edited clone generated by CRISPR-Cas9, carrying a partial deletion of the second exon that harbors a key domain and the SYNGR2 p.Arg63Cys, was associated with a lower viral titer compared to wildtype PK15 cells (\u3e24 hpi) and supernatant (\u3e48hpi)(P \u3c 0.05). Identification of a non-conservative substitution in this key domain of SYNGR2 suggests that the SYNGR2 p.Arg63Cys variant may underlie the observed genetic effect on viral load

Re-interpreting conventional interval estimates taking into account bias and extra-variation

BACKGROUND: The study design with the smallest bias for causal inference is a perfect randomized clinical trial. Since this design is often not feasible in epidemiologic studies, an important challenge is to model bias properly and take random and systematic variation properly into account. A value for a target parameter might be said to be "incompatible" with the data (under the model used) if the parameter's confidence interval excludes it. However, this "incompatibility" may be due to bias and/or extra-variation. DISCUSSION: We propose the following way of re-interpreting conventional results. Given a specified focal value for a target parameter (typically the null value, but possibly a non-null value like that representing a twofold risk), the difference between the focal value and the nearest boundary of the confidence interval for the parameter is calculated. This represents the maximum correction of the interval boundary, for bias and extra-variation, that would still leave the focal value outside the interval, so that the focal value remained "incompatible" with the data. We describe a short example application concerning a meta analysis of air versus pure oxygen resuscitation treatment in newborn infants. Some general guidelines are provided for how to assess the probability that the appropriate correction for a particular study would be greater than this maximum (e.g. using knowledge of the general effects of bias and extra-variation from published bias-adjusted results). SUMMARY: Although this approach does not yet provide a method, because the latter probability can not be objectively assessed, this paper aims to stimulate the re-interpretation of conventional confidence intervals, and more and better studies of the effects of different biases